Workplace Technical Operations Lead

Drive the successful delivery of large-scale workplace technology programs across complex, cross-functional environments. You will lead program planning and execution, establish clear milestones and dependencies, manage risks and resources, and ensure teams remain aligned to delivery commitments. Using automation, analytics, and modern work management practices, you will improve program visibility, predictability, and efficiency. You will work closely with senior stakeholders, engineering teams, business partners, and vendors to translate strategy into measurable results.

As a Workplace Technical Operations Lead in Workplace Technology, you lead the operational rhythm and delivery governance for complex, multi-workstream technology initiatives that support workplace and Retail Branch programs. You translate business objectives into executable roadmaps, measurable milestones, and transparent reporting. You improve delivery efficiency by applying workflow automation, advanced analytics, and fit-for-purpose tooling. You balance outcomes across scope, schedule, financial stewardship, risk management, and stakeholder experience.

You serve as a strategic partner to program and product stakeholders and coordinate across engineering, infrastructure, end-user technology, finance, service operations, vendors, and business partners. You create standards that improve consistency and quality across planning, execution, and controls. You also help teams adopt approved artificial intelligence (AI)-assisted engineering practices with clear validation expectations for correctness, performance, and security.

• Lead end-to-end delivery governance for large, cross-functional workplace technology programs from initiation through implementation.
• Define program operating cadence, milestones, dependencies, and success metrics to improve predictability and accountability. Implement workflow automation to reduce manual reporting, improve data quality, and accelerate decision-making.
• Build portfolio dashboards and executive-ready reporting using advanced analytics and automated data pipelines.
• Manage program financials, including forecasting, budget tracking, capitalization planning inputs, and variance commentary.
• Partner with risk and control functions to ensure delivery practices align to required controls, documentation, and audit readiness.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
• Drive continuous improvement initiatives that streamline intake, planning, resource forecasting, and delivery processes. Coordinate delivery across internal teams and external vendors to resolve blockers and maintain alignment on outcomes.
• Mentor team members and promote a culture of operational excellence, innovation, and inclusive collaboration.
• Establish expectations for safe and compliant use of approved AI-assisted engineering tools, including validation standards and secure handling of inputs and outputs.
• Communicate clearly with stakeholders at all levels, escalating risks early and proposing actionable options.

• Five years of experience leading complex technology programs, infrastructure deployments, or enterprise transformation initiatives.
• Demonstrated experience managing multi-workstream delivery across cross-functional stakeholders and vendors.
• Proven ability to improve execution using automation, data analytics, and modern work management practices.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and…